A new method for crop classification combining time series of radar images and crop phenology information

Remote Sensing of Environment

Volumn 198, Issue , 2017, Pages 369-383

  Bargiel, Damian ^a  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Agriculture; Classification; Phenology; Radar; Sentinel 1

Indexed keywords

AGRICULTURE; BIOLOGY; CROPS; DECISION TREES; FORESTRY; IMAGE CLASSIFICATION; MAXIMUM LIKELIHOOD; RADAR; RADAR IMAGING; SUGAR BEETS; TIME SERIES; VEGETATION;

AGRICULTURAL LAND USE; CLASSIFICATION ACCURACY; CLASSIFICATION APPROACH; CLASSIFICATION METHODS; COMPLETE CLASSIFICATION; LAND COVER CLASSIFICATION; PHENOLOGY; SENTINEL-1;

CLASSIFICATION (OF INFORMATION);

AGRICULTURAL LAND; CEREAL; IMAGE CLASSIFICATION; LAND COVER; LAND USE CHANGE; PERFORMANCE ASSESSMENT; PHENOLOGY; RADAR; RANDOM WALK METHOD; SENTINEL; SUGAR BEET; TIME SERIES; WEED;

GERMANY;

BETA VULGARIS SUBSP. VULGARIS; BRASSICA NAPUS VAR. NAPUS; HORDEUM; SECALE CEREALE; SOLANUM TUBEROSUM; ZEA MAYS;

EID: 85021639792     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.06.022     Document Type: Article

References (48)

1. Adamowicz, W., Akçakaya, H., Arcenas, A., Babu, S., Balh, D., Confalonieri, U., Cramer, W., Falconí, F., Fritz, S., Green, R., Gutíerrez-Espeleta, E., Hamilton, K., Kane, R., Latham, J., Matthews, E., Ricketts, T., Tian Xian, Y., Analytical approaches for assessing ecosystem condition and human well-being. Assessment, M.E., (eds.) Global Assessment Reports Volume: 1 Current State & Trends Assessment chapter 2, 2005, Island Press, 37–71.
2. Analytics, R., Weston, S., foreach: Provides Foreach Looping Construct for R. 2015 https://CRAN.R-project.org/package=foreach R package version 1.4.3.
3. Bargiel, D., Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes. Ecol. Inf. 13 (2013), 9–16, 10.1016/j.ecoinf.2012.10.004 http://www.sciencedirect.com/science/article/pii/S1574954112001021.
4. Bargiel, D., Herrmann, S., Multi-temporal land-cover classification of agricultural areas in two European regions with high resolution spotlight TerraSAR-X Data. Remote Sens. 3 (2011), 859–877, 10.3390/rs3050859 http://www.mdpi.com/2072-4292/3/5/859/.
5. Bivand, R., Keitt, T., Rowlingson, B., rgdal: Bindings for the Geospatial Data Abstraction Library. 2016 https://CRAN.R-project.org/package=rgdal R package version 1.1-10.
6. Bivand, R., Lewin-Koh, N., maptools: Tools for Reading and Handling Spatial Objects. 2016 https://CRAN.R-project.org/package=maptools R package version 0.8-39.
7. Breiman, L., Random Forests. Mach. Learn. 45 (2001), 5–32.
8. Conradsen, K., Nielsen, A.A., Skriver, H., Determining the points of change in time series of polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 54 (2016), 3007–3024, 10.1109/TGRS.2015.2510160.
9. Deutscher Wetterdienst. Mittelwerte der Temperatur und des Niederschlags bezogen auf den aktuellen Standort. 2012 http://www.dwd.de Online: http://www.dwd.de.
10. Erten, E., Lopez-Sanchez, J.M., Yuzugullu, O., Hajnsek, I., Retrieval of agricultural crop height from space: a comparison of sartechniques. Remote Sens. Environ. 187 (2016), 130–144, 10.1016/j.rse.2016.10.007 http://www.sciencedirect.com/science/article/pii/S0034425716303790.
11. FAO. A Review of the Current State of Bioenergy Development in G8 +5 Countries. Food and Agriculture Organization of the United Nations, 2007.
12. Feng, X., Fu, B., Yang, X., Lü, Y., Remote sensing of ecosystem services: an opportunity for spatially explicit assessment. Chin. Geogr. Sci. 20 (2010), 522–535.
13. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K., Global consequences of land use. Science 309 (2005), 570–574.
14. Guarini, R., Bruzzone, L., Santoni, M., Vuolo, F., Dini, L., Analysis of the potentiality of multi-temporal COSMO-SkyMed data for classifying summer crops. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016, 3170–3173.
15. Hijmans, R.J., raster: Geographic Data Analysis and Modeling. 2016 https://CRAN.R-project.org/package=raster R package version 2.5-8.
16. Jiao, X., Kovacs, J.M., Shang, J., McNairn, H., Walters, D., Ma, B., Geng, X., Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data. ISPRS J. Photogramm. Remote Sens. 96 (2014), 38–46, 10.1016/j.isprsjprs.2014.06.014 http://www.sciencedirect.com/science/article/pii/S0924271614001695.
17. Julea, A., Méger, N., Rigotti, C., Trouvé, E., Bolon, P., Lăzărescu, V., Mining pixel evolutions in satellite image time series for agricultural monitoring. Perner, P., (eds.) Advances in Data Mining. Applications and Theoretical Aspects chapter Mining Pixel Evolutions in Satellite Image Time Series for Agricultural Monitoring, 2011, Springer Berlin Heidelberg, Berlin, Heidelberg, 189–203.
18. Kenduiywo, B.K., Bargiel, D., Soergel, U., Spatial-temporal conditional random fields crop classification from TerraSAR-X Images. ISPRS Annals Photogramm. Remote Sens. Spat. Inf. Sci., 2, 2015.
19. Kenduiywo, B.K., Bargiel, D., Soergel, U., Crop type mapping from a sequence of TerraSAR-X images with dynamic conditional random fields. ISPRS Annals Photogramm. Remote Sens. Spat. Inf. Sci., 2016, 59–66.
20. Küçük, C., Taşkin, G., Erten, E., Paddy-rice phenology classification based on machine-learning methods using multitemporal co-polar X-Band SAR Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9 (2016), 2509–2519.
21. Larra naga, A., Álvarez Mozos, J., On the added value of Quad-Pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery. Remote Sens., 2016.
22. Leite, P.B.C., Feitosa, R.Q., Formaggio, A.R., da Costa, G.A.O.P., Pakzad, K., Sanches, I.D., Hidden Markov models for crop recognition in remote sensing image sequences. Pattern Recogn. Lett. 32 (2011), 19–26, 10.1016/j.patrec.2010.02.008.
23. Liaw, A., Wiener, M., Classification and regression by Random Forest. R News 2 (2002), 18–22 http://CRAN.R-project.org/doc/Rnews/.
24. Lopez-Sanchez, J., Cloude, S., Ballester-Berman, J., Rice phenology monitoring by means of SAR polarimetry at X-Band. IEEE Trans. Geosci. Remote Sens. 50 (2012), 2695–2709.
25. Lopez-Sanchez, J.M., Vicente-Guijalba, F., Ballester-Berman, J.D., Cloude, S.R., Polarimetric response of rice fields at C-Band: analysis and phenology retrieval. IEEE Trans. Geosci. Remote Sens. 52 (2014), 2977–2993, 10.1109/TGRS.2013.2268319.
26. Mascolo, L., Lopez-Sanchez, J.M., Vicente-Guijalba, F., Nunziata, F., Migliaccio, M., Mazzarella, G., A complete procedure for crop phenology estimation with PolSAR data based on the complex Wishart classifier. IEEE Trans. Geosci. Remote Sens. 54 (2016), 6505–6515.
27. Matson, P.A., Parton, W.J., Power, A.G., Swift, M.J., Agricultural intensification and ecosystem properties. Science 277:5325 (1997), 504–509.
28. McNairn, H., Champagne, C., Shang, J., Holmstrom, D., Reichert, G., Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS J. Photogramm. Remote Sens. 64 (2009), 434–449, 10.1016/j.isprsjprs.2008.07.006 http://www.sciencedirect.com/science/article/pii/S0924271608000944.
29. McNairn, H., Ellis, J., Van Der Sanden, J.J., Hirose, T., Brown, R.J., Providing crop information using RADARSAT-1 and satellite optical imagery. Int. J. Remote Sens. 23 (2002), 851–870, 10.1080/01431160110070753.
30. McNairn, H., Kross, A., Lapen, D., Caves, R., Shang, J., Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2. Int. J. Appl. Earth Obs. Geoinf. 28 (2014), 252–259, 10.1016/j.jag.2013.12.015 http://www.sciencedirect.com/science/article/pii/S0303243413001839.
31. McNairn, H., Shang, J., Champagne, C., Jiao, X., TerraSAR-X and RADARSAT-2 for crop classification and acreage estimation. 2009 IEEE International Geoscience & Remote Sensing Symposium, Proceedings, vol. 5, 2009, 898–901.
32. Meier, U., Bleiholder, H., Growth stages of mono-and dicotyledonous plants. BBCH Monograph, 2006, Federal Biological Research Centre for Agriculture and Forestry.
33. Navarro, A., Rolim, J., Miguel, I., Catalão, J., Silva, J., Painho, M., Vekerdy, Z., Crop Monitoring based on SPOT-5 Take-5 and Sentinel-1A data for the estimation of crop water requirements. Remote Sens., 8, 2016, 525, 10.3390/rs8060525 http://www.mdpi.com/2072-4292/8/6/525.
34. Pebesma, E., Bivand, R., Classes and methods for spatial data in R. R News 5 (2005), 9–13 http://CRAN.R-project.org/doc/Rnews/.
35. Poh Sze Choo, H., Cooper, D., Devendra, C., Dixon, J., Gaskell, J., Khan, S., Lal, R., Lipper, L., Pretty, J., Primavera, J., Ramankutty, N., Viglizzo, E., Wiebe, K., Cultivated systems. Assessment, M.E., (eds.) Global Assessment Reports Volume: 1 Current State & Trends Assessment chapter 26, 2005, Island Press, 747–787.
36. Analytics, Revolution, Weston, S., doSNOW: Foreach Parallel Adaptor for the ‘snow’ Package. 2015 https://CRAN.R-project.org/package=doSNOW R package version 1.0.14.
37. Sentinel-1 Team, E., Sentinel-1 User Handbook. 2013, European Space Agency.
38. Siachalou, S., Mallinis, G., Tsakiri-Strati, M., A Hidden Markov models approach for crop classification: linking crop phenology to time series of multi-sensor remote sensing data. Remote Sens., 7, 2015, 3633, 10.3390/rs70403633 http://www.mdpi.com/2072-4292/7/4/3633.
39. Skriver, H., Crop classification by multitemporal C- and L-Band single- and dual-polarization and fully polarimetric SAR. IEEE Trans. Geosci. Remote Sens. 50 (2012), 2138–2149.
40. Skriver, H., Mattia, F., Satalino, G., Balenzano, A., Pauwels, V.R.N., Verhoest, N.E.C., Davidson, M., Crop Classification Using Short-Revisit Multitemporal SAR Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4 (2011), 423–431.
41. Sonobe, R., Tani, H., Wang, X., Kobayashi, N., Shimamura, H., Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data. Remote Sens. Lett. 5 (2014), 157–164 % tt arXiv:http://dx.doi.org/10.1080/2150704X.2014.889863.
42. Sonobe, R., Tani, H., Wang, X., Kobayashi, N., Shimamura, H., Discrimination of crop types with TerraSAR-X-derived information. Phys. Chem. Earth Parts A/B/C 8384 (2015), 2–13 Emerging science and applications with microwave remote sensing data.
43. Tivy, J., Landwirtschaft und Umwelt. - Agrarökosysteme in der Biosphäre. 1993, Spektrum Akademischer Verlag GmbH Heidelberg, Berlin, Oxford, 344.
44. Vicente-Guijalba, F., Martinez-Marin, T., Lopez-Sanchez, J.M., Dynamical approach for real-time monitoring of agricultural crops. IEEE Trans. Geosci. Remote Sens. 53 (2015), 3278–3293, 10.1109/TGRS.2014.2372897.
45. Villa, P., Stroppiana, D., Fontanelli, G., Azar, R., Brivio, P.A., In-season mapping of crop type with optical and X-Band SAR data: a classification tree approach using synoptic seasonal features. Remote Sens., 7, 2015, 12859, 10.3390/rs71012859 http://www.mdpi.com/2072-4292/7/10/12859.
46. Weihs, C., Ligges, U., Luebke, K., Raabe, N., klaR analyzing German business cycles. Baier, D., Decker, R., Schmidt-Thieme, L., (eds.) Data Analysis and Decision Support, 2005, Springer-Verlag, Berlin, 335–343.
47. Yuzugullu, O., Erten, E., Hajnsek, I., Rice growth monitoring by means of X-Band co-polar SAR: feature clustering and BBCH scale. IEEE Geosci. Remote Sens. Lett. 12 (2015), 1218–1222.
48. Yuzugullu, O., Erten, E., Hajnsek, I., Estimation of rice crop height from X- and C-band PolSAR by metamodel-based optimization. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10 (2017), 194–204, 10.1109/JSTARS.2016.2575362.